Rolling-element bearings are often used in critical applications, wherein their failure in service would result in significant commercial loss to the end-user. It is therefore highly relevant to be able to predict the residual life of a bearing, in order to plan intervention in a way that avoids failure in service, while minimizing the losses that may arise from taking the machinery in question out of service to replace the bearing.
The service life of a rolling-element bearing is generally determined by fatigue of the operating surfaces as a result of repeated stresses in operational use. Fatigue failure of a rolling-element bearing results from progressive flaking or pitting of the surfaces of the rolling elements and of the surfaces of the corresponding bearing races. The flaking and pitting cause seizure of one or more of the rolling elements, which in turn generates excessive heat, pressure and friction.
Bearings are selected for a specific application on the basis of a calculated or predicted life expectance compatible with the expected type of service in the application. The length of a service life can be predicted from the nominal operating conditions of speed, load carried, lubrication conditions, etc. For example, a so-called “L-10 life” is the life expectancy in hours during which at least 90% of a specific group of bearings under specific load conditions will still be in service.
However, this type of life prediction is considered inadequate for the purpose of maintenance planning for several reasons. One reason is that the actual operation conditions may be quite different from the nominal conditions used for design purposes. Another reason is that the bearing's life may be radically compromised by short-duration events or unplanned events, such as overloads, lubrication failures, installation errors, etc. Yet another reason is that, even if design operating conditions are accurately reproduced in service, the inherently random character of the fatigue process give rise to large statistical variations in the actual service life of nominally identical bearings.
In order to improve maintenance planning, it is common practice to monitor the values of physical quantities related to vibrations and temperature in operational use, so as to be able to detect the first signs of impending failure. This monitoring is often referred to as “condition monitoring”. Condition monitoring brings various benefits. A first benefit is that the user is warned of deterioration in the condition of the bearing in a controlled way, thus minimizing the commercial impact. A second benefit is that condition monitoring helps to identify poor installation or poor operating practices, e.g., misalignment, imbalance, high vibration, etc., that will reduce the life of the bearing if left uncorrected.
European patent application publication EP 1164550 describes an example of a condition monitoring system for monitoring the presence or absence of an abnormality in a machine component such as, for example, a bearing having rolling elements.